Transaction data scheduling

Abstract

A transaction data scheduling method and apparatus, a storage medium, a product and an electronic device. The method comprises: on the basis of historical data conversion records, performing conversion prediction processing, so as to obtain data conversion prediction information for a preset future time point (S102); on the basis of the data conversion prediction information and a credit data change target for the preset future time point, obtaining a credit data demand volume for the future time point (S104); on the basis of an existing transaction data acquisition plan, obtaining a transaction data predicted volume for the preset future time point (S106); on the basis of the transaction data predicted volume and the credit data demand volume, calculating a predicted transaction data gap for the preset future time point (S108); and acquiring a new transaction data acquisition plan satisfying the predicted transaction data gap, the new transaction data acquisition plan being used for acquiring transaction data to fill the predicted transaction data gap (S110).

Description

Transaction data arrangement Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to the arrangement of transaction data. Background Technology

[0002] Financial institutions can perform transaction data conversion operations such as lending with users, thereby converting transaction data such as cash into credit data such as debt. In order to ensure that financial institutions can cope with market fluctuations and manage risks while possessing credit data, they need to hold a portion of transaction data for risk management. Although financial institutions can calculate the transaction data they currently hold and determine whether they have the ability to cope with the risks that credit data may bring, the relevant technologies cannot predict future risks or make contingency plans for future risks. Therefore, a transaction data arrangement method that can predict transaction data gaps and plan the acquisition of transaction data is needed. Summary of the Invention

[0003] This disclosure provides a method, apparatus, storage medium, product, and electronic device for transaction data arrangement. It calculates transaction data gaps at preset future time points by using historical data conversion records and existing transaction data acquisition plans, thereby proactively adding transaction data acquisition plans to address future data gaps and achieving the goal of managing risks and arranging transaction data in advance. The technical solution is as follows.

[0004] In a first aspect, embodiments of this disclosure provide a method for arranging transaction data, the method comprising: performing conversion prediction processing based on historical data conversion records to obtain data conversion prediction information for a preset future time point; obtaining the credit data demand for the future time point based on the data conversion prediction information and a credit data change target for the preset future time point; obtaining the predicted transaction data quantity for the preset future time point based on an existing transaction data acquisition plan; calculating the predicted transaction data gap for the preset future time point based on the predicted transaction data quantity and the credit data demand; and obtaining a new transaction data acquisition plan to satisfy the predicted transaction data gap, wherein the new transaction data acquisition plan is used to acquire transaction data to fill the predicted transaction data gap.

[0005] Secondly, embodiments of this disclosure provide a transaction data arrangement apparatus, the apparatus comprising: a conversion prediction module, configured to perform conversion prediction processing based on historical data conversion records to obtain data conversion prediction information for a preset future time point; a credit demand calculation module, configured to obtain the credit data demand for the future time point based on the data conversion prediction information and the credit data change target for the preset future time point; a transaction data prediction module, configured to obtain the predicted transaction data quantity for the preset future time point based on an existing transaction data acquisition plan; a gap calculation module, configured to calculate the predicted transaction data gap for the preset future time point based on the predicted transaction data quantity and the credit data demand; and a transaction data acquisition module, configured to acquire a new transaction data acquisition plan to satisfy the predicted transaction data gap, the new transaction data acquisition plan being used to acquire transaction data to fill the predicted transaction data gap.

[0006] Thirdly, embodiments of this disclosure provide a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the above-described method steps.

[0007] Fourthly, embodiments of this disclosure provide a computer program product that stores multiple instructions adapted for loading by a processor and executing the above-described method steps.

[0008] Fifthly, embodiments of this disclosure provide an electronic device that may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to execute the above-described method steps.

[0009] In one or more embodiments of this disclosure, conversion prediction processing is performed based on historical data conversion records to obtain data conversion prediction information for a preset future time point. Based on the data conversion prediction information and the credit data change target for the preset future time point, the credit data demand for the future time point is obtained. Based on the existing transaction data acquisition plan, the predicted transaction data volume for the preset future time point is obtained. Based on the predicted transaction data volume and the credit data demand, the predicted transaction data gap for the preset future time point is calculated. A new transaction data acquisition plan to meet the predicted transaction data gap is obtained. The new transaction data acquisition plan is used to acquire transaction data to fill the predicted transaction data gap. By calculating the transaction data gap for the preset future time point using historical data conversion records and the existing transaction data acquisition plan, a new transaction data acquisition plan can be added in advance to address the future transaction data gap, achieving the purpose of managing risks and pre-planning transaction data. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments or related technologies of this disclosure, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 is a schematic diagram illustrating an example of financial institution transaction data conversion provided in an embodiment of this disclosure;

[0012] Figure 2 is a flowchart illustrating a transaction data arrangement method provided in an embodiment of this disclosure;

[0013] Figure 3 is a flowchart illustrating a transaction data arrangement method provided in an embodiment of this disclosure;

[0014] Figure 4 is a schematic diagram illustrating an example of determining the target transaction data acquisition quota according to an embodiment of this disclosure;

[0015] Figure 5 is a schematic diagram of a transaction data arrangement device provided in an embodiment of this disclosure;

[0016] Figure 6 is a schematic diagram of the structure of a transaction data acquisition module provided in an embodiment of this disclosure;

[0017] Figure 7 is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0018] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this disclosure.

[0019] Financial institutions can perform data conversion operations with users. These operations can involve converting transactional data such as lending and loan repayments between the financial institution and the user. When a user lacks transactional data, the financial institution can lend it to the user. This transactional data can be tradable assets such as currency or goods. In return, the financial institution holds an equivalent amount of credit data. This credit data can be credit assets such as debt or assets used as collateral by the user. For example, if a user borrows 50,000 from a financial institution, the institution needs to lend 50,000 worth of transactional data and hold 50,000 worth of credit data. If the user repays 30,000, meaning the user repays 30,000 worth of transactional data, the financial institution will then hold 30,000 worth of transactional data and 20,000 worth of credit data. To cope with risks such as market volatility, financial institutions need to hold a certain amount of transaction data in addition to credit data to ensure normal data conversion operations with users. For example, this prevents situations where a user needs to borrow from a financial institution, but the institution lacks sufficient transaction data to lend. This portion of transaction data is called "position," which represents the quantity of assets such as currency and securities held by the financial institution. It serves as a reserve to cope with potential risks and uncertainties, and its size can also be used to measure the financial institution's risk tolerance. Understandably, a larger position indicates a stronger risk tolerance, while a smaller position indicates a weaker risk tolerance. However, since positions are used to cope with uncertain risks, financial institutions do not use the transaction data in the position for lending, wealth management, or other operations. Therefore, an excessively large position can reduce the efficiency of the financial institution's transaction data growth.

[0020] Therefore, in order to ensure both the risk-bearing capacity of financial institutions and the efficiency of transaction data growth, a transaction data arrangement device can be used to calculate the required position size of financial institutions according to a pre-set allocation ratio. The pre-set allocation ratio represents the ratio between the transaction data volume and the application data volume held by the financial institution, and can be set by the financial institution according to relevant regulations. The transaction data arrangement method provided in this disclosure embodiment can be implemented using a computer program and can run on a transaction data arrangement device based on the von Neumann architecture. This computer program can be integrated into applications or run as a standalone utility application.

[0021] Please refer to Figure 1, which is an example of a financial institution transaction data conversion provided in this embodiment of the present disclosure. It is understood that due to the increasing demand for credit data by financial institutions and changes in the amount of user lending and repayment transaction data, the amount of credit data held by financial institutions will change over time. If the amount of credit data increases but the amount of transaction data in the position held by the financial institution does not meet the pre-set repayment rate, the financial institution will have a transaction data gap, resulting in a lack of risk tolerance in the future. Financial institutions need to use transaction data acquisition channels to obtain transaction data to fill the transaction data gap. Transaction data acquisition channels are the ways in which financial institutions obtain transaction data, such as interbank financing, interbank lending and repayment, shareholder lending and repayment, etc. The transaction data arrangement device can predict potential transaction data gaps that financial institutions may encounter at preset future time points and generate transaction data acquisition plans to fill these gaps, thereby enhancing the financial institutions' risk tolerance at those preset future time points. These preset future time points are specific future dates and can be either the initial settings of the transaction data arrangement device or settings made by the financial institution, such as 400 days from now. The transaction data acquisition plan can include specifying which transaction data acquisition channels to acquire and how much transaction data to acquire. There can be one or multiple preset future time points, such as each day within the next 400 days. The transaction data arrangement device can predict potential transaction data gaps at each preset future time point and generate transaction data acquisition plans for all such gaps.

[0022] The transaction data arrangement method provided in this disclosure will be described in detail below with reference to specific embodiments.

[0023] Please refer to Figure 2, which is a flowchart illustrating a transaction data arrangement method provided in this embodiment of the present disclosure. As shown in Figure 2, the method of this embodiment may include the following steps S102-S110.

[0024] S102, perform conversion prediction processing based on historical data conversion records to obtain data conversion prediction information for a preset future time point.

[0025] Specifically, the transaction data arrangement device can acquire historical data conversion records between financial institutions and all users. These records contain all transaction data conversion operations that have occurred between financial institutions and all users, including the time of the conversion, whether it was a loan or a repayment, and the amount of transaction data involved. By learning users' habits of lending and repaying transaction data, the device can perform conversion prediction processing based on historical data conversion records to obtain transaction data conversion prediction information for a preset future time point. This prediction information includes how much transaction data a user may have lent and how much transaction data they may have repaid at the preset future time point, facilitating subsequent calculations of the amount of transaction data and credit data that the financial institution may hold at that time point.

[0026] S104, based on data transformation prediction information and the credit data change target for a preset future time point, obtain the credit data demand at the future time point.

[0027] Specifically, financial institutions can set credit data change targets based on operational conditions and the growth of transaction data. These targets represent the financial institution's desired credit data volume at a predetermined future time. Therefore, the transaction data scheduling device can obtain the credit data demand at the predetermined future time based on data transformation prediction information and the credit data change target. This demand is the amount of credit data the financial institution is predicted to achieve at that future time. For example, if the amount of credit data held by the financial institution, calculated based on the data transformation prediction information, is less than the credit data change target, then the demand can be considered the target. Conversely, if the amount of credit data held by the financial institution, calculated based on the data transformation prediction information, is greater than the target, then the transaction data scheduling device can directly recognize the amount of credit data calculated based on the data transformation prediction information as the demand.

[0028] S106, Based on the existing transaction data acquisition plan, obtain the predicted amount of transaction data at a preset future time point.

[0029] Specifically, the transaction data arrangement device can acquire the financial institution's existing transaction data acquisition plan. This plan refers to the financial institution's current transaction data acquisition strategy, which may be in the stage where transaction data has been borrowed from transaction data acquisition channels but has not yet been fully repaid. Since the transaction data acquisition plan can include information such as which transaction data acquisition channels were borrowed from and how much transaction data was borrowed, it can also include the time required for full repayment, the amount of transaction data repaid each time, the repayment time, and the transaction data acquisition cost, where the cost can be the interest required by the transaction data acquisition channels. Therefore, based on the existing transaction data acquisition plan, the transaction data arrangement device can obtain the financial institution's predicted transaction data volume at a preset future time point. This predicted transaction data volume is the amount of transaction data the financial institution will hold at the preset future time point, as predicted by the transaction data arrangement device.

[0030] S108, calculate the predicted transaction data gap at a preset future time point based on the predicted transaction data volume and the credit data demand volume.

[0031] Specifically, the transaction data arrangement device can calculate the predicted transaction data gap at a preset future time point based on the predicted transaction data volume and credit demand. For example, the transaction data arrangement device can calculate the transaction data demand at a preset future time point based on the preset payment rate and credit data demand. The transaction data demand is the size of the position that a financial institution can have in terms of risk tolerance when holding credit data of the credit demand. If the predicted transaction data volume is less than the transaction data demand, the transaction data arrangement device can calculate the difference between the predicted transaction data volume and the transaction data demand to obtain the predicted transaction data gap at the preset future time point.

[0032] S110, Obtain a new transaction data acquisition plan to meet the predicted transaction data gap. The new transaction data acquisition plan is used to acquire transaction data to fill the predicted transaction data gap.

[0033] Specifically, the transaction data arrangement device can acquire new transaction data acquisition plans for transaction data gaps. These plans can be used to acquire transaction data to fill the predicted transaction data gaps at a preset future time point, thereby enabling financial institutions to have the capacity to bear risks at the preset future time point.

[0034] In this embodiment, conversion prediction processing is performed based on historical data conversion records to obtain data conversion prediction information for a preset future time point. Based on the data conversion prediction information and the credit data change target for the preset future time point, the credit data demand for the future time point is obtained. Based on the existing transaction data acquisition plan, the predicted transaction data volume for the preset future time point is obtained. Based on the predicted transaction data volume and the credit data demand, the predicted transaction data gap for the preset future time point is calculated. A new transaction data acquisition plan to meet the predicted transaction data gap is obtained, and the new transaction data acquisition plan is used to acquire transaction data to fill the predicted transaction data gap. By calculating the transaction data gap for the preset future time point using historical data conversion records and the existing transaction data acquisition plan, a new transaction data acquisition plan can be added in advance to address the future transaction data gap, achieving the purpose of managing risks and pre-planning transaction data.

[0035] Please refer to Figure 3, which is a flowchart illustrating a transaction data arrangement method provided in an embodiment of this disclosure. As shown in Figure 3, the method in this embodiment may include the following steps S202-S220.

[0036] S202, perform conversion prediction processing based on historical data conversion records to obtain data conversion prediction information for a preset future time point.

[0037] Specifically, the transaction data arrangement device can acquire historical data conversion records between financial institutions and all users. These records contain all transaction data conversion operations that have occurred between financial institutions and all users, including the time of the conversion, whether it was a loan or a repayment, and the amount of transaction data involved. By learning users' habits of lending and repaying transaction data, the device can perform conversion prediction processing based on historical data conversion records to obtain transaction data conversion prediction information for a preset future time point. This prediction information includes how much transaction data a user may have lent and how much transaction data they may have repaid at the preset future time point, facilitating subsequent calculations of the amount of transaction data and credit data that the financial institution may hold at that time point.

[0038] S204, based on the existing amount of credit data and data transformation prediction information, obtain the predicted amount of credit data at a preset future time point.

[0039] Specifically, the transaction data arrangement device can obtain the amount of transaction data lent and repaid by users from the current time point to a preset future time point based on data conversion prediction information. The amount of transaction data lent is the amount of transaction data lent by users to financial institutions during this period, and the amount of transaction data repaid is the amount of transaction data repaid by users to financial institutions during this period. Subtracting the amount of transaction data repaid from the amount of transaction data lent yields the amount of credit data change from the current time point to the preset future time point. Then, the transaction data arrangement device can obtain the current amount of credit data held by financial institutions, that is, the amount of credit data held by financial institutions at the current time point. Adding the amount of credit data and the amount of credit data change yields the predicted amount of credit data at the preset future time point. The predicted amount of credit data is the amount of credit data that financial institutions may hold at the preset future time point, as predicted by the transaction data arrangement device.

[0040] S206, based on the transaction data change targets and predicted credit data volume for a preset future time point, obtain the credit data demand corresponding to the preset future time point.

[0041] Specifically, financial institutions can set credit data change targets based on operational needs and the growth of transaction data. These targets indicate the financial institution's desired credit data volume to reach the target at a predetermined future time. Therefore, the transaction data scheduling device can determine the credit data demand at the predetermined future time based on the predicted credit data volume and the target credit data change. This demand is the amount of credit data the financial institution is predicted to achieve at that future time. If the predicted credit data volume exceeds the target, the transaction data scheduling device can directly recognize the credit data volume calculated based on the data conversion prediction information as the credit data demand. Conversely, if the predicted credit data volume is less than the target, the transaction data scheduling device can recognize the demand as the target credit data change.

[0042] S208, based on the existing transaction data acquisition plan, obtain the predicted amount of transaction data at a preset future time point.

[0043] Specifically, the transaction data arrangement device can acquire the financial institution's existing transaction data acquisition plan. This plan refers to the financial institution's current transaction data acquisition strategy, which may be in the stage where transaction data has been borrowed from transaction data acquisition channels but has not yet been fully repaid. Since the transaction data acquisition plan can include information such as which transaction data acquisition channels were borrowed from and how much transaction data was borrowed, it can also include the time required for full repayment, the amount of transaction data repaid each time, the repayment time, and the transaction data acquisition cost, where the cost can be the interest required by the transaction data acquisition channels. Therefore, based on the existing transaction data acquisition plan, the transaction data arrangement device can obtain the financial institution's predicted transaction data volume at a preset future time point. This predicted transaction data volume is the amount of transaction data the financial institution will hold at the preset future time point, as predicted by the transaction data arrangement device.

[0044] S210: Based on the credit data demand and the pre-deposit rate, obtain the transaction data demand corresponding to a preset future time point.

[0045] Specifically, the transaction data arrangement device can calculate the transaction data demand at a preset future time point based on the pre-set payment rate and credit data demand. The transaction data demand is the size of the position that a financial institution can have in terms of risk tolerance when holding credit data of the required credit demand. If the predicted transaction data is less than the transaction data demand, the transaction data arrangement device can calculate the difference between the predicted transaction data and the transaction data demand to obtain the predicted transaction data gap at the preset future time point.

[0046] Optionally, the pre-equipment rate can be the ratio of the transaction data demand to the sum of the transaction data demand and the credit data demand. For example, it can be 5%. If the credit quantity demand is 950,000, then the transaction data demand can be 50,000.

[0047] S212, calculate the difference between the required amount of transaction data and the predicted amount of transaction data to obtain the predicted transaction data gap at a preset future time point.

[0048] Specifically, if the demand for transaction data exceeds the predicted amount of transaction data, it means that at a predetermined future time, the amount of transaction data that a financial institution can hold is insufficient to cover its credit data stream positions and cannot cope with potential risks. The transaction data arrangement device can calculate the difference between the demand for transaction data and the predicted amount of transaction data to obtain the predicted transaction data gap at the predetermined future time. The predicted transaction data gap is the amount of transaction data that the financial institution will lack at the predetermined future time, as predicted by the transaction data arrangement device. Only by filling the predicted transaction data gap at the predetermined future time can the financial institution have the capacity to bear risks at that time.

[0049] S214, Generate the target transaction data acquisition plan corresponding to each preset transaction data acquisition channel according to the preset transaction data acquisition channel and concentration requirements.

[0050] Specifically, the transaction data arrangement device can acquire new transaction data acquisition plans for transaction data gaps. These plans can be used to acquire transaction data to fill predicted gaps at a predetermined future time. Financial institutions can set preset transaction data acquisition channels for the device, which can be one or more, such as interbank financing, interbank lending, and shareholder lending. Concentration requirements can be the degree of concentration of transaction data across all acquisition channels as determined by relevant staff or the financial institution. This can serve as a constraint on the transaction data acquisition plans generated by the device, meaning the device does not generate plans randomly but rather generates plans that meet the concentration requirements for the financial institution. The device can generate target transaction data acquisition plans for each preset channel based on the preset acquisition channels and concentration requirements. There can be one or more target plans, each targeting only one channel.

[0051] Optionally, financial institutions cannot obtain transaction data from preset transaction data acquisition channels without limit. These channels can be configured with a maximum lending limit, which represents the maximum amount of transaction data that a financial institution can simultaneously lend from that channel. Different preset transaction data acquisition channels can have different maximum limits set for different financial institutions. To ensure that the transaction data acquisition plan generated by the transaction data arrangement device does not exceed the maximum limit of each preset transaction data acquisition channel, the device can obtain the remaining and used limits of each preset transaction data acquisition channel at a preset future time point. The used limit represents the amount of transaction data that the financial institution will lend to the preset transaction data acquisition channel at that preset future time point, and the remaining limit represents the maximum amount of transaction data that the financial institution can still lend to the preset transaction data acquisition channel at that preset future time point. The remaining limit can be the difference between the maximum limit and the used limit. Concentration requirements may include quota ratio requirements, which can be the ratio of used quota to available quota for each preset transaction data acquisition channel. The transaction data allocation device can generate a target transaction data acquisition quota for each preset transaction data acquisition channel based on the quota ratio requirements, remaining quota, and used quota. The target transaction data acquisition quota is the amount of transaction data that can be borrowed from each preset transaction data acquisition channel under the concentration requirements. The target transaction data acquisition quota is less than or equal to the remaining quota, and the ratio of the sum of the used quota and the target transaction data acquisition quota for each preset transaction data acquisition channel meets the quota ratio requirements. Then, the transaction data allocation device can generate a target transaction data acquisition plan for each preset transaction data acquisition channel based on the transaction data acquisition quota. The target transaction data acquisition plan represents the amount of transaction data that financial institutions can borrow from the preset transaction data acquisition channels to meet the target transaction data acquisition quota.

[0052] Optionally, the credit limit ratio requirement can be a requirement for the ratio of used credit limits among various preset transaction data channels. For example, if the transaction data distribution device is set up with three transaction data acquisition channels, namely channel A, channel B, and channel C, then the credit limit ratio requirement can be that the used credit limits of channel A, channel B, and channel C must maintain a ratio of 1:2:3. That is, if a financial institution has not yet acquired transaction data from any transaction data acquisition channel, and needs to acquire transaction data worth 60,000, then it needs to acquire transaction data worth 10,000 from channel A, 20,000 from channel B, and 30,000 from channel C, so that the used credit limits for each preset transaction data acquisition channel meet the credit limit ratio requirement. The credit limit ratio requirement can also be a requirement for the ratio of the used credit limit of each transaction data acquisition channel to the total used credit limit of all channels. The total used credit limit of all channels can be the sum of the used credit limits of all transaction data acquisition channels. For example, it can be that the used credit limit of each preset transaction data acquisition channel must not exceed 50% of the total used credit limit of all channels.

[0053] It is understandable that obtaining transaction data from preset transaction data acquisition channels incurs transaction data acquisition costs, which can be the interest charged by these channels. It is also understandable that the transaction data acquisition costs may differ between different preset transaction data acquisition channels. The transaction data allocation device, while acquiring the target transaction data acquisition quota for each preset transaction data acquisition channel based on quota ratio requirements, remaining quota, and used quota, can also minimize the total transaction data acquisition cost. The total transaction data acquisition cost is the sum of the transaction data acquisition costs that a financial institution needs to pay to all preset transaction data acquisition channels when borrowing the target transaction data quota amount.

[0054] For example, please refer to Figure 4, which is a schematic diagram illustrating an example of determining the target transaction data acquisition limit in an embodiment of this disclosure. The transaction data arrangement device is configured with three transaction data acquisition channels, namely channel A, channel B, and channel C. The transaction data arrangement device obtains that at a preset future time point, the used amount of channel A, channel B, and channel C are 4w, 3w, and 5w, respectively, and the remaining amount of channel A, channel B, and channel C are 6w, 5w, and 1w, respectively. The transaction data arrangement device calculates that at the preset future time point, the predicted transaction data gap of the financial institution is 8w. The concentration requirement requires that the used amount of each preset transaction data acquisition channel shall not exceed 50% of the total used amount of all channels. Among these, the transaction data acquisition cost of borrowing transaction data from channel A is the lowest, followed by channel C, and the transaction data acquisition cost of borrowing transaction data from channel B is the highest. Although Channel A has the lowest transaction data acquisition cost and a remaining quota of 60,000, acquiring another 60,000 in transaction data from Channel A would exceed 50% of the total used quota across all channels. To meet concentration requirements, the financial institution can only acquire a maximum of 40,000 in transaction data from Channel A. Therefore, the target transaction data acquisition quota for Channel A is 40,000. While Channel C has a lower transaction data acquisition cost than Channel B, it only has a remaining quota of 10,000. The financial institution can only acquire a maximum of 10,000 in transaction data from Channel C. Therefore, the target transaction data acquisition quota for Channel C is 10,000. The sum of the target transaction data acquisition quotas for Channel C and Channel A is 50,000, which is insufficient to fill the predicted transaction data gap. The financial institution still needs to acquire 30,000 in transaction data from Channel B. Thus, the target transaction data acquisition quota for Channel B is 30,000. This approach achieves a target transaction data acquisition quota that satisfies concentration requirements while minimizing the total transaction data acquisition cost.

[0055] Optionally, the transaction data arrangement device can employ a data acquisition quota generation model to obtain the target transaction data acquisition quota for each preset transaction data acquisition channel. This model outputs the target transaction data acquisition quota for each preset transaction data acquisition channel that satisfies the quota ratio requirement, the remaining quota, and minimizes the total transaction data acquisition cost. The transaction data arrangement device can obtain the transaction data acquisition cost corresponding to each preset transaction data acquisition channel, and then input the quota ratio requirement, the remaining quota, the used quota, and the transaction data acquisition cost into the transaction data acquisition quota generation model. The model can then output the target transaction data acquisition quota for each preset transaction data acquisition channel.

[0056] S216, calculate the sum of all target transaction data acquisition quotas to obtain the new transaction data acquisition quota.

[0057] Specifically, the transaction data arrangement device can obtain the sum of all target transaction data acquisition quotas, thereby obtaining the new transaction data acquisition quota.

[0058] S218, determine whether the amount of newly added transaction data acquired is greater than the predicted transaction data gap.

[0059] Specifically, the target transaction data acquisition plan generated by the transaction data arrangement device may still fail to fill the predicted transaction data gap, and it is necessary to make another judgment to increase the accuracy of the target transaction data acquisition plan. The transaction data arrangement device can determine whether the amount of newly acquired transaction data is greater than the predicted transaction data gap. If yes, then step S220 is executed; if no, then step S214 is executed.

[0060] S220, confirm all target transaction data acquisition plans as new transaction data acquisition plans.

[0061] Specifically, if the amount of newly acquired transaction data is greater than the predicted transaction data gap, it means that the target transaction data acquisition plan can fill the predicted transaction data gap. The transaction data arrangement device can identify all target transaction data acquisition plans as new transaction data acquisition plans. The new transaction data acquisition plans can be used to acquire transaction data to fill the predicted transaction data gap at a preset future time point, thereby enabling financial institutions to have the capacity to bear risks at the preset future time point.

[0062] In this embodiment of the disclosure, conversion prediction processing is performed based on historical data conversion records to obtain data conversion prediction information for a preset future time point. Based on the existing credit data volume and the data conversion prediction information, the predicted credit data volume for the preset future time point is obtained. Based on the transaction data change target for the preset future time point and the predicted credit data volume, the credit data demand corresponding to the preset future time point is obtained. The credit data demand is obtained by combining the predicted credit data volume with the transaction data change target, so that the generated transaction data acquisition plan is more in line with the development needs of the financial institution itself. Based on the existing transaction data acquisition plan, the predicted transaction data volume for a preset future time point is obtained. Based on the credit data demand and the pre-deposit rate, the corresponding transaction data demand for that preset future time point is obtained. The difference between the transaction data demand and the predicted transaction data volume is calculated to obtain the predicted transaction data gap for that preset future time point. Target transaction data acquisition plans are generated for each preset transaction data acquisition channel according to preset transaction data acquisition channels and concentration requirements. This approach minimizes transaction data acquisition costs while meeting concentration requirements, improving the effectiveness of the transaction data deployment device. Furthermore, a transaction data acquisition quota generation model can be used to obtain the target transaction data acquisition quota for each preset transaction data acquisition channel, improving the convenience and intelligence of target transaction data acquisition quota calculation. The sum of all target transaction data acquisition quotas is calculated to obtain the new transaction data acquisition quota. It is then determined whether the new transaction data acquisition quota is greater than the predicted transaction data gap. This second determination increases the accuracy of the target transaction data acquisition plan. If it is greater than the preset transaction data gap, all target transaction data acquisition plans are confirmed as new transaction data acquisition plans. By converting historical data into records and calculating existing transaction data acquisition plans, the transaction data gaps at preset future time points are determined. This allows for the creation of new transaction data acquisition plans in advance to address future transaction data gaps, thereby achieving the goal of managing risks and proactively arranging transaction data.

[0063] The transaction data arrangement device provided in the embodiments of this disclosure will now be described in detail with reference to Figures 5 and 6. It should be noted that the transaction data arrangement device in Figures 5 and 6 is used to execute the method of the embodiments shown in Figures 1-4 of this disclosure. For ease of explanation, only the parts related to the embodiments of this disclosure are shown. For specific technical details not disclosed, please refer to the embodiments shown in Figures 1-4 of this disclosure.

[0064] Please refer to Figure 5, which shows a schematic diagram of the structure of a transaction data arrangement device provided in an exemplary embodiment of this disclosure. This transaction data arrangement device can be implemented as all or part of a device through software, hardware, or a combination of both. The device 1 includes a conversion prediction module 11, a credit demand calculation module 12, a transaction data prediction module 13, a gap calculation module 14, and a transaction data acquisition module 15.

[0065] The conversion prediction module 11 is used to perform conversion prediction processing based on historical data conversion records to obtain data conversion prediction information for a preset future time point; the credit demand calculation module 12 is used to obtain the credit data demand for the future time point based on the data conversion prediction information and the credit data change target for the preset future time point; optionally, the credit demand calculation module 12 is specifically used to obtain the predicted credit data volume for the preset future time point based on the existing credit data volume and the data conversion prediction information; and to obtain the credit data demand corresponding to the preset future time point based on the transaction data change target for the preset future time point and the predicted credit data volume.

[0066] The transaction data prediction module 13 is used to obtain the predicted transaction data amount at the preset future time point based on the existing transaction data acquisition plan; the gap calculation module 14 is used to calculate the predicted transaction data gap at the preset future time point based on the predicted transaction data amount and the credit data demand amount; optionally, the gap calculation module 14 is specifically used to obtain the transaction data demand amount corresponding to the preset future time point based on the credit data demand amount and the pre-delivery rate; calculate the difference between the transaction data demand amount and the predicted transaction data amount to obtain the predicted transaction data gap at the preset future time point.

[0067] The transaction data acquisition module 15 is used to acquire a new transaction data acquisition plan that satisfies the predicted transaction data gap. The new transaction data acquisition plan is used to acquire transaction data to fill the predicted transaction data gap.

[0068] Specifically, please refer to Figure 6, which is a schematic diagram of the structure of a transaction data acquisition module provided in this embodiment of the present disclosure. As shown in Figure 6, the transaction data acquisition module 15 may include: a target plan acquisition unit 151, a judgment unit 152, a new plan determination unit 153, and a plan continuation generation unit 154.

[0069] The target plan acquisition unit 151 is used to generate a target transaction data acquisition plan corresponding to each of the preset transaction data acquisition channels based on preset transaction data acquisition channels and concentration requirements. Optionally, the target plan acquisition unit 151 is specifically used to acquire the remaining quota and used quota of each preset transaction data acquisition channel at the preset future time point; acquire the quota ratio requirement in the concentration requirements, the quota ratio requirement being a ratio requirement for the used quota of each of the preset transaction data acquisition channels; generate a target transaction data acquisition quota for each of the preset transaction data acquisition channels based on the quota ratio requirement, the remaining quota, and the used quota, wherein the target transaction data acquisition quota is less than or equal to the remaining quota, and the ratio of the used quota of each preset transaction data acquisition channel to the sum of the target transaction data acquisition quota meets the quota ratio requirement; and generate a target transaction data acquisition plan corresponding to each of the preset transaction data acquisition channels based on the target transaction data acquisition quota.

[0070] Optionally, the target plan acquisition unit 151 is specifically used to acquire the transaction data acquisition cost corresponding to each of the preset transaction data acquisition channels; based on the quota ratio requirement, the remaining quota, the used quota, and the transaction data acquisition cost, it uses a transaction data acquisition quota generation model to obtain the target transaction data acquisition quota for each of the preset transaction data acquisition channels; wherein, the transaction data acquisition quota generation model is used to output the target transaction data acquisition quota for each of the preset transaction data acquisition channels that satisfies the quota ratio requirement, the remaining quota, and minimizes the total transaction data acquisition cost.

[0071] The judgment unit 152 is used to calculate the sum of all the target transaction data acquisition quotas to obtain the new transaction data acquisition quota; if the new transaction data acquisition quota is greater than or equal to the predicted transaction data gap, then it is determined that all the target transaction data acquisition plans meet the predicted transaction data gap.

[0072] The new plan determination unit 153 is used to confirm all the target transaction data acquisition plans as new transaction data acquisition plans if all the target transaction data acquisition plans meet the predicted transaction data gap; the plan continuing generation unit 154 is used to execute the step of generating target transaction data acquisition plans corresponding to each preset transaction data acquisition channel according to the preset transaction data acquisition channels and concentration requirements if the target transaction data acquisition plans do not meet the predicted transaction data gap.

[0073] In this embodiment, conversion prediction processing is performed based on historical data conversion records to obtain data conversion prediction information for a preset future time point. Based on the existing credit data volume and the data conversion prediction information, the predicted credit data volume for the preset future time point is obtained. Based on the transaction data change target for the preset future time point and the predicted credit data volume, the credit data demand corresponding to the preset future time point is obtained. The credit data demand is obtained by combining the predicted credit data volume with the transaction data change target, making the generated transaction data acquisition plan more in line with the development needs of the financial institution itself. Based on the existing transaction data acquisition plan, the predicted transaction data volume for a preset future time point is obtained. Based on the credit data demand and the pre-deposit rate, the corresponding transaction data demand for that preset future time point is obtained. The difference between the transaction data demand and the predicted transaction data volume is calculated to obtain the predicted transaction data gap for that preset future time point. Target transaction data acquisition plans are generated for each preset transaction data acquisition channel according to preset transaction data acquisition channels and concentration requirements. This approach minimizes transaction data acquisition costs while meeting concentration requirements, improving the effectiveness of the transaction data deployment device. Furthermore, a transaction data acquisition quota generation model can be used to obtain the target transaction data acquisition quota for each preset transaction data acquisition channel, improving the convenience and intelligence of target transaction data acquisition quota calculation. The sum of all target transaction data acquisition quotas is calculated to obtain the new transaction data acquisition quota. It is then determined whether the new transaction data acquisition quota is greater than the predicted transaction data gap. This second determination increases the accuracy of the target transaction data acquisition plan. If it is greater than the preset transaction data gap, all target transaction data acquisition plans are confirmed as new transaction data acquisition plans. By converting historical data into records and calculating existing transaction data acquisition plans, the transaction data gaps at preset future time points are determined. This allows for the creation of new transaction data acquisition plans in advance to address future transaction data gaps, thereby achieving the goal of managing risks and proactively arranging transaction data.

[0074] It should be noted that the transaction data arrangement device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the transaction data arrangement method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the transaction data arrangement device and the transaction data arrangement method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0075] The sequence numbers of the embodiments disclosed above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0076] This disclosure also provides a computer storage medium that can store multiple instructions. These instructions are adapted to be loaded and executed by a processor using the transaction data arrangement method described in the embodiments shown in Figures 1-4 above. For details of the execution process, please refer to the specific descriptions of the embodiments shown in Figures 1-4, which will not be repeated here.

[0077] This disclosure also provides a computer program product that stores at least one instruction, which is loaded by the processor and executed as described in the embodiments shown in Figures 1-4 above. For the specific execution process, please refer to the detailed description of the embodiments shown in Figures 1-4, which will not be repeated here.

[0078] Please refer to Figure 7, which shows a structural block diagram of an electronic device provided in an exemplary embodiment of this disclosure. The electronic device of this disclosure may include one or more components such as a processor 110, a memory 120, an input device 130, an output device 140, and a bus 150. The processor 110, memory 120, input device 130, and output device 140 may be connected to each other via the bus 150.

[0079] Processor 110 may include one or more processing cores. Processor 110 connects to various parts of the electronic device using various interfaces and lines, and executes various functions of terminal 100 and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 120, and by calling data stored in memory 120. Optionally, processor 110 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 110 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user page, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into processor 110 and may be implemented separately using a communication chip.

[0080] The memory 120 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 120 may include non-transitory computer-readable storage medium. The memory 120 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 120 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the various method embodiments described above, etc. The operating system may be the Android system, including systems deeply developed based on the Android system, the iOS system developed by Apple Inc., including systems deeply developed based on the iOS system, or other systems.

[0081] The memory 120 can be divided into operating system space and user space. The operating system runs in the operating system space, while native and third-party applications run in user space. To ensure that different third-party applications can achieve good running performance, the operating system allocates corresponding system resources for each application. However, different application scenarios within the same third-party application have different requirements for system resources. For example, in local resource loading scenarios, third-party applications have high requirements for disk read speed; in animation rendering scenarios, third-party applications have high requirements for GPU performance. Since the operating system and third-party applications are independent of each other, the operating system often cannot promptly perceive the current application scenario of a third-party application, resulting in the operating system's inability to adapt system resources accordingly.

[0082] In order for the operating system to distinguish the specific application scenarios of third-party applications, it is necessary to establish data communication between the third-party applications and the operating system. This would allow the operating system to obtain the current scenario information of the third-party applications at any time, and then perform targeted system resource adaptation based on the current scenario.

[0083] The input device 130 is used to receive input instructions or data, and includes, but is not limited to, a keyboard, mouse, camera, microphone, or touch device. The output device 140 is used to output instructions or data, and includes, but is not limited to, a display device and a speaker. In one example, the input device 130 and the output device 140 can be combined, and the input device 130 and the output device 140 can be a touch display screen.

[0084] The touch display screen can be designed as a full-screen, curved screen, or irregularly shaped screen. It can also be designed as a combination of a full-screen and a curved screen, or a combination of an irregularly shaped screen and a curved screen; this disclosure does not limit the specific design.

[0085] In addition, those skilled in the art will understand that the structure of the electronic device shown in the above figures does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, the electronic device may also include radio frequency circuits, input units, sensors, audio circuits, Wireless Fidelity (WiFi) modules, power supplies, Bluetooth modules, etc., which will not be described in detail here.

[0086] In the electronic device shown in Figure 7, the processor 110 can be used to call the transaction data arrangement application stored in the memory 120, and specifically perform the following operations: perform conversion prediction processing based on historical data conversion records to obtain data conversion prediction information for a preset future time point; based on the data conversion prediction information and the credit data change target for the preset future time point, obtain the credit data demand for the future time point; obtain the predicted transaction data quantity for the preset future time point based on the existing transaction data acquisition plan; calculate the predicted transaction data gap for the preset future time point based on the predicted transaction data quantity and the credit data demand; obtain a new transaction data acquisition plan to meet the predicted transaction data gap, wherein the new transaction data acquisition plan is used to acquire transaction data to fill the predicted transaction data gap.

[0087] In one embodiment, when the processor 110 executes the operation of obtaining the credit data demand at the future time point based on the data transformation prediction information and the credit data change target for the preset future time point, it specifically performs the following operations: obtaining the predicted credit data volume for the preset future time point based on the existing credit data volume and the data transformation prediction information; and obtaining the credit data demand corresponding to the preset future time point based on the transaction data change target for the preset future time point and the predicted credit data volume.

[0088] In one embodiment, when the processor 110 calculates the predicted transaction data gap at a preset future time point based on the predicted transaction data amount and the credit data demand amount, it specifically performs the following operations: based on the credit data demand amount and the pre-delivery rate, it obtains the transaction data demand amount corresponding to the preset future time point; it calculates the difference between the transaction data demand amount and the predicted transaction data amount to obtain the predicted transaction data gap at the preset future time point.

[0089] In one embodiment, when the processor 110 executes a new transaction data acquisition plan that satisfies the predicted transaction data gap, it specifically performs the following operations: generating a target transaction data acquisition plan corresponding to each preset transaction data acquisition channel based on preset transaction data acquisition channels and concentration requirements; if all the target transaction data acquisition plans satisfy the predicted transaction data gap, then all the target transaction data acquisition plans are confirmed as new transaction data acquisition plans; if the target transaction data acquisition plans do not satisfy the predicted transaction data gap, then the step of generating a target transaction data acquisition plan corresponding to each preset transaction data acquisition channel based on preset transaction data acquisition channels and concentration requirements is executed.

[0090] In one embodiment, when the processor 110 executes the generation of target transaction data acquisition plans corresponding to each preset transaction data acquisition channel based on preset transaction data acquisition channels and concentration requirements, it specifically performs the following operations: obtaining the remaining and used quotas of each preset transaction data acquisition channel at the preset future time point; obtaining the quota ratio requirement in the concentration requirements, wherein the quota ratio requirement is a ratio requirement for the used quota of each preset transaction data acquisition channel; generating a target transaction data acquisition quota for each preset transaction data acquisition channel based on the quota ratio requirement, the remaining quota, and the used quota, wherein the target transaction data acquisition quota is less than or equal to the remaining quota, and the ratio of the used quota of each preset transaction data acquisition channel to the sum of the target transaction data acquisition quota meets the quota ratio requirement; and generating a target transaction data acquisition plan corresponding to each preset transaction data acquisition channel based on the target transaction data acquisition quota.

[0091] In one embodiment, when the processor 110 generates the target transaction data acquisition quota for each of the preset transaction data acquisition channels based on the quota ratio requirement, the remaining quota, and the used quota, it specifically performs the following operations: obtaining the transaction data acquisition cost corresponding to each of the preset transaction data acquisition channels; and obtaining the target transaction data acquisition quota for each of the preset transaction data acquisition channels using a transaction data acquisition quota generation model based on the quota ratio requirement, the remaining quota, the used quota, and the transaction data acquisition cost; wherein the transaction data acquisition quota generation model is used to output the target transaction data acquisition quota for each of the preset transaction data acquisition channels that satisfies the quota ratio requirement, the remaining quota, and minimizes the total transaction data acquisition cost.

[0092] In one embodiment, before executing the operation of confirming all target transaction data acquisition plans as new transaction data acquisition plans if all target transaction data acquisition plans meet the predicted transaction data gap, the processor 110 further performs the following operations: calculating the sum of all target transaction data acquisition quotas to obtain a new transaction data acquisition quota; and determining that all target transaction data acquisition plans meet the predicted transaction data gap if the new transaction data acquisition quota is greater than or equal to the predicted transaction data gap.

[0093] In this embodiment, conversion prediction processing is performed based on historical data conversion records to obtain data conversion prediction information for a preset future time point. Based on the existing credit data volume and the data conversion prediction information, the predicted credit data volume for the preset future time point is obtained. Based on the transaction data change target for the preset future time point and the predicted credit data volume, the credit data demand corresponding to the preset future time point is obtained. The credit data demand is obtained by combining the predicted credit data volume with the transaction data change target, making the generated transaction data acquisition plan more in line with the development needs of the financial institution itself. Based on the existing transaction data acquisition plan, the predicted transaction data volume for a preset future time point is obtained. Based on the credit data demand and the pre-deposit rate, the corresponding transaction data demand for that preset future time point is obtained. The difference between the transaction data demand and the predicted transaction data volume is calculated to obtain the predicted transaction data gap for that preset future time point. Target transaction data acquisition plans are generated for each preset transaction data acquisition channel according to preset transaction data acquisition channels and concentration requirements. This approach minimizes transaction data acquisition costs while meeting concentration requirements, improving the effectiveness of the transaction data deployment device. Furthermore, a transaction data acquisition quota generation model can be used to obtain the target transaction data acquisition quota for each preset transaction data acquisition channel, improving the convenience and intelligence of target transaction data acquisition quota calculation. The sum of all target transaction data acquisition quotas is calculated to obtain the new transaction data acquisition quota. It is then determined whether the new transaction data acquisition quota is greater than the predicted transaction data gap. This second determination increases the accuracy of the target transaction data acquisition plan. If it is greater than the preset transaction data gap, all target transaction data acquisition plans are confirmed as new transaction data acquisition plans. By converting historical data into records and calculating existing transaction data acquisition plans, the transaction data gaps at preset future time points are determined. This allows for the creation of new transaction data acquisition plans in advance to address future transaction data gaps, thereby achieving the goal of managing risks and proactively arranging transaction data.

[0094] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory, or random access memory, etc.

[0095] The above-disclosed embodiments are merely preferred embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Therefore, any equivalent variations made in accordance with the claims of this disclosure shall still fall within the scope of this disclosure.

[0096] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in the embodiments of this specification are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the historical data conversion records and credit data requirements involved in this specification were obtained under full authorization.

Claims

1. A method for arranging transaction data, the method comprising: Based on historical data conversion records, conversion prediction processing is performed to obtain data conversion prediction information for preset future time points; Based on the data conversion prediction information and the credit data change target for the preset future time point, the credit data demand at the future time point is obtained. The predicted amount of transaction data at the preset future time point is obtained based on the existing transaction data acquisition plan; Calculate the predicted transaction data gap at the preset future time point based on the predicted transaction data volume and the credit data demand volume; Obtain a new transaction data acquisition plan to meet the predicted transaction data gap, the new transaction data acquisition plan being used to acquire transaction data to fill the predicted transaction data gap.

2. The method according to claim 1, wherein obtaining the credit data demand at the future time point based on the data transformation prediction information and the credit data change target for the preset future time point includes: Based on the existing amount of credit data and the data transformation prediction information, the predicted amount of credit data at the preset future time point is obtained; Based on the transaction data change target for the preset future time point and the predicted credit data volume, the credit data demand corresponding to the preset future time point is obtained.

3. The method according to claim 1, wherein calculating the predicted transaction data gap at the preset future time point based on the predicted transaction data volume and the credit data demand volume includes: Based on the credit data demand and the pre-deposit rate, obtain the transaction data demand corresponding to the preset future time point; Calculate the difference between the required amount of transaction data and the predicted amount of transaction data to obtain the predicted transaction data gap at the preset future time point.

4. The method according to claim 1, wherein obtaining the new transaction data acquisition plan that satisfies the predicted transaction data gap includes: Generate a target transaction data acquisition plan corresponding to each preset transaction data acquisition channel based on the preset transaction data acquisition channels and concentration requirements; If all the target transaction data acquisition plans meet the predicted transaction data gap, then all the target transaction data acquisition plans are confirmed as new transaction data acquisition plans; If the target transaction data acquisition plan does not meet the predicted transaction data gap, then the step of generating the target transaction data acquisition plan corresponding to each preset transaction data acquisition channel according to the preset transaction data acquisition channel and concentration requirements is executed.

5. The method according to claim 4, wherein generating the target transaction data acquisition plan corresponding to each preset transaction data acquisition channel based on preset transaction data acquisition channels and concentration requirements includes: When the preset future time point is obtained, the remaining quota and used quota of each preset transaction data acquisition channel; The quota ratio requirement in the concentration requirement is the ratio requirement of the used quota for each of the preset transaction data acquisition channels; Based on the quota ratio requirement, the remaining quota, and the used quota, a target transaction data acquisition quota is generated for each of the preset transaction data acquisition channels. The target transaction data acquisition quota is less than or equal to the remaining quota. The ratio of the used quota to the sum of the target transaction data acquisition quota for each preset transaction data acquisition channel meets the quota ratio requirement. Based on the target transaction data acquisition quota, a target transaction data acquisition plan is generated for each of the preset transaction data acquisition channels.

6. The method according to claim 5, wherein generating the target transaction data acquisition quota for each of the preset transaction data acquisition channels based on the quota ratio requirement, the remaining quota, and the used quota includes: Obtain the transaction data acquisition cost corresponding to each of the preset transaction data acquisition channels; Based on the quota ratio requirement, the remaining quota, the used quota, and the transaction data acquisition cost, the target transaction data acquisition quota for each of the preset transaction data acquisition channels is obtained using a transaction data acquisition quota generation model. The transaction data acquisition quota generation model is used to output the target transaction data acquisition quota for each of the preset transaction data acquisition channels that meets the quota ratio requirement, the remaining quota, and minimizes the total transaction data acquisition cost.

7. The method according to claim 4, further comprising, before confirming all target transaction data acquisition plans as new transaction data acquisition plans if all target transaction data acquisition plans satisfy the predicted transaction data gap: Calculate the sum of all the target transaction data acquisition quotas to obtain the new transaction data acquisition quota; If the newly added transaction data acquisition amount is greater than or equal to the predicted transaction data gap, then it is determined that all the target transaction data acquisition plans meet the predicted transaction data gap.

8. A transaction data arrangement device, the device comprising: The conversion prediction module is used to perform conversion prediction processing based on historical data conversion records to obtain data conversion prediction information for preset future time points; The credit demand calculation module is used to obtain the credit data demand at the future time point based on the data conversion prediction information and the credit data change target for the preset future time point; The transaction data prediction module is used to obtain the predicted amount of transaction data at the preset future time point based on the existing transaction data acquisition plan; The gap calculation module is used to calculate the predicted transaction data gap at the preset future time point based on the predicted transaction data volume and the credit data demand volume. The transaction data acquisition module is used to acquire a new transaction data acquisition plan that satisfies the predicted transaction data gap. The new transaction data acquisition plan is used to acquire transaction data to fill the predicted transaction data gap.

9. A computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the method steps of any one of claims 1 to 7.

10. A computer program product storing a plurality of instructions adapted for loading by a processor and executing the method steps of any one of claims 1 to 7.

11. An electronic device, comprising: A processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and executed the method steps as claimed in any one of claims 1 to 7.

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